Visual quality control is a demanding task of increasing importance in industrial manufacturing. Both speed and flexibility are of paramount importance for viable and competitive inspection systems. In our work we developed a dedicated neural network architecture for anomaly detection that can easily be trained by a single presentation of examples and is amenable to massively parallel VLSI implementation. Our focus is on our ASIC and prototype system design effort for this network. Keywords: Neural networks, VLSI, novelty filter, anomaly detection, automated visual industrial quality control.